根据情境动态评估多模态数据可信度，提升融合准确性。
Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuits
- 用条件概率电路建模每条数据的模态可信度
- 在高噪声下比静态可信度方法准确率提升29%
- 适合需要可解释性与鲁棒融合的多模态应用
多模态融合需整合可能因情境冲突的多个信息源。现有方法通常依赖静态的源可靠性假设，难以应对传感器退化或类别特异性干扰导致的模态不可靠问题。本文提出C²MF框架，利用条件概率电路（CPC）对实例级源可靠性进行建模。通过基于KL散度的上下文特定信息可信度（CSIC）量化可靠性，该度量可精确计算于CPC中，并将传统静态可信度估计作为特例推广。为评估跨模态冲突下的鲁棒性，我们构建了冲突基准（Conflict benchmark），通过类别特异性扰动制造模态间差异。实验表明，在高噪声环境下，C²MF相比静态可靠性基线，预测准确率最高提升29%，同时保持概率电路融合的可解释性优势。
原文摘要 · Abstract (English)
Multimodal fusion requires integrating information from multiple sources that may conflict depending on context. Existing fusion approaches typically rely on static assumptions about source reliability, limiting their ability to resolve conflicts when a modality becomes unreliable due to situational factors such as sensor degradation or class-specific corruption. We introduce C$^2$MF, a context-specfic credibility-aware multimodal fusion framework that models per-instance source reliability using a Conditional Probabilistic Circuit (CPC). We formalize instance-level reliability through Context-Specific Information Credibility (CSIC), a KL-divergence-based measure computed exactly from the CPC. CSIC generalizes conventional static credibility estimates as a special case, enabling principled and adaptive reliability assessment. To evaluate robustness under cross-modal conflicts, we propose the Conflict benchmark, in which class-specific corruptions deliberately induce discrepancies between different modalities. Experimental results show that C$^2$MF improves predictive accuracy by up to 29% over static-reliability baselines in high-noise settings, while preserving the interpretability advantages of probabilistic circuit-based fusion.
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